Machine-Learning Applications in Thrombosis and Hemostasis

Abstract

The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applications that we can no longer do without, such as navigation apps or translation software. However, many people are still unsure if ML algorithms should be used in medicine in their current form. Doctors are doubtful to what extent they can trust the predictions of algorithms. Shortcomings in development and unclear regulatory oversight can lead to bias, inequality, applicability concerns, and nontransparent assessments. Past mistakes, however, have led to a better understanding of what is needed to develop effective models for clinical use. Physicians and clinical researchers must participate in all development phases and understand their pitfalls. In this review, we explain the basic concepts of ML, present examples in the field of thrombosis and hemostasis, discuss common pitfalls, and present a methodological framework that can be used to develop effective algorithms.

Keywords

machine learning - artificial intelligence - thrombosis - hemostasis

Publikationsverlauf

Eingereicht: 15. August 2024

Angenommen: 19. September 2024

Artikel online veröffentlicht:
05. November 2024

• References

• 1 Alpaydin E. Machine Learning, Revised and Updated Edition. MIT Press; 2021
• 2 Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A Primer for the epidemiologist. Am J Epidemiol 2019; 188 (12) 2222-2239
• 3 Gandhi TK, Classen D, Sinsky CA. et al. How can artificial intelligence decrease cognitive and work burden for front line practitioners?. JAMIA Open 2023; 6 (03) ooad079
• 4 Heinrichs B, Eickhoff SB. Your evidence? Machine learning algorithms for medical diagnosis and prediction. Hum Brain Mapp 2020; 41 (06) 1435-1444
• 5 Alvarado R. Should we replace radiologists with deep learning? Pigeons, error and trust in medical AI. Bioethics 2022; 36 (02) 121-133
• 6 Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics 2021; 22 (01) 122
• 7 Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med 2023; 6 (01) 120
• 8 Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci U S A 2020; 117 (23) 12592-12594
• 9 Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA 2019; 322 (24) 2377-2378
• 10 Wong A, Otles E, Donnelly JP. et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med 2021; 181 (08) 1065-1070
• 11 Wong A, Cao J, Lyons PG. et al. Quantification of sepsis model alerts in 24 US hospitals before and during the COVID-19 pandemic. JAMA Netw Open 2021; 4 (11) e2135286
• 12 Habib AR, Lin AL, Grant RW. The epic sepsis model falls short-the importance of external validation. JAMA Intern Med 2021; 181 (08) 1040-1041
• 13 Schwartz JM, Moy AJ, Rossetti SC, Elhadad N, Cato KD. Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: a scoping review. J Am Med Inform Assoc 2021; 28 (03) 653-663
• 14 Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001; 23 (01) 89-109
• 15 Deo RC. Machine learning in medicine. Circulation 2015; 132 (20) 1920-1930
• 16 Korteling JEH, van de Boer-Visschedijk GC, Blankendaal RAM, Boonekamp RC, Eikelboom AR. Human- versus artificial intelligence. Front Artif Intell 2021; 4: 622364
• 17 Kuhn M, Johnson K. Applied Predictive Modeling. Accessed August 11, 2022 at: https://link.springer.com/book/10.1007/978-1-4614-6849-3
• 18 Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol 2019; 19 (01) 64
• 19 Alam MM, Islam MT. Machine learning approach of automatic identification and counting of blood cells. Healthc Technol Lett 2019; 6 (04) 103-108
• 20 Singh A, Thakur N, Sharma A. A review of supervised machine learning algorithms. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). 2016: 1310-1315 . Accessed January 12, 2024 at: https://ieeexplore.ieee.org/abstract/document/7724478/authors#authors
• 21 Nilius H, Cuker A, Haug S. et al. A machine-learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: a prospective, multicenter, observational study. EClinicalMedicine 2022; 55: 101745
• 22 Loh WY, Cao L, Zhou P. Subgroup identification for precision medicine: a comparative review of 13 methods. Wiley Interdiscip Rev Data Min Knowl Discov 2019; 9 (05) e1326
• 23 Alloghani M, Al-Jumeily D, Mustafina J, Hussain A, Aljaaf AJ. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In: Berry MW, Mohamed A, Yap BW. eds. Supervised and Unsupervised Learning for Data Science. Springer International Publishing; 2020: 3-21
• 24 Weller BE, Bowen NK, Faubert SJ. Latent class analysis: a guide to best practice. J Black Psychol 2020; 46 (04) 287-311
• 25 Pallares Robles A, Ten Cate V, Lenz M. et al. Unsupervised clustering of venous thromboembolism patients by clinical features at presentation identifies novel endotypes that improve prognostic stratification. Thromb Res 2023; 227: 71-81
• 26 Zhang Z. written on behalf of AME Big-Data Clinical Trial Collaborative Group. Reinforcement learning in clinical medicine: a method to optimize dynamic treatment regime over time. Ann Transl Med 2019; 7 (14) 345
• 27 Nemati S, Ghassemi MM, Clifford GD. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC);. 2016: 2978-2981
• 28 Zhu X, Goldberg AB. Introduction to Semi-Supervised Learning. Springer Nature; 2022
• 29 Omiye JA, Gui H, Rezaei SJ, Zou J, Daneshjou R. Large language models in medicine: the potentials and pitfalls : a narrative review. Ann Intern Med 2024; 177 (02) 210-220
• 30 Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med 2023; 29 (08) 1930-1940
• 31 Open AI, Achiam J, Adler S. et al. GPT-4 Technical Report. Published online March 4, 2024
• 32 Li Y, Li Z, Zhang K, Dan R, Jiang S, Zhang Y. ChatDoctor: a medical chat model fine-tuned on a large language model meta-AI (LLaMA) using medical domain knowledge. Cureus 2023; 15 (06) e40895
• 33 Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus 2023; 15 (02) e35179
• 34 Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health 2021; 3 (03) e195-e203
• 35 Cool Stuff Now Epic and Generative AI / Epic. Accessed July 24, 2024 at: https://www.epic.com/epic/post/cool-stuff-now-epic-and-generative-ai
• 36 Ayers JW, Poliak A, Dredze M. et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med 2023; 183 (06) 589-596
• 37 Health C for D and R. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. FDA. Published online October 20, 2023. Accessed November 30, 2023 at: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machinelearning-aiml-enabled-medical-devices
• 38 Nafee T, Gibson CM, Travis R. et al. Machine learning to predict venous thrombosis in acutely ill medical patients. Res Pract Thromb Haemost 2020; 4 (02) 230-237
• 39 Zaboras Z, Jørgensen CT, Stensvold A. et al. Real-world data on treatment patterns and bleeding in cancer-associated thrombosis: data from the TROLL Registry. TH Open 2024; 8 (01) e132-e140
• 40 Weissler EH, Naumann T, Andersson T. et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials 2021; 22 (01) 537
• 41 Lam BD, Chrysafi P, Chiasakul T. et al. Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis. Blood Adv 2024; 8 (12) 2991-3000
• 42 Niemiec E. Will the EU Medical Device Regulation help to improve the safety and performance of medical AI devices?. Digit Health 2022; 8: 20 552076221089079
• 43 EUDAMED database - EUDAMED. Accessed July 24, 2024 at: https://ec.europa.eu/tools/eudamed/#/screen/home
• 44 Joshi G, Jain A, Araveeti SR, Adhikari S, Garg H, Bhandari M. FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape. Electronics (Basel) 2024; 13 (03) 498
• 45 Digital Cell Imaging System. Scopio Labs. Accessed July 24, 2024 at: https://scopiolabs.com/imaging-platforms/
• 46 Katz BZ, Feldman MD, Tessema M. et al. Evaluation of Scopio Labs X100 Full Field PBS: the first high-resolution full field viewing of peripheral blood specimens combined with artificial intelligence-based morphological analysis. Int J Lab Hematol 2021; 43 (06) 1408-1416
• 47 Dale DC, Kelley ML, Navarro-De La Vega M. et al. A novel device suitable for home monitoring of white blood cell and neutrophil counts. Blood 2018; 132 (Suppl. 01) 1103
• 48 23andMe. Is Hereditary Thrombophilia Genetic? Genetic Testing for Hereditary Thrombophilia - 23andMe - 23andMe. Accessed July 24, 2024 at: https://www.23andme.com/topics/health-predispositions/hereditary-thrombophilia/
• 49 Kolev M, Horn MP, Semmo N, Nagler M. Rational development and application of biomarkers in the field of autoimmunity: a conceptual framework guiding clinicians and researchers. J Transl Autoimmun 2022; 5: 100151
• 50 Nagler M, Nilius H. Next-generation diagnostic instruments in haematology. Br J Haematol 2023; 202 (05) 925-927
• 51 Nagler M. Translating laboratory tests into clinical practice: a conceptual framework. Hamostaseologie 2020; 40 (04) 420-429
• 52 Nilius H, Tsouka S, Nagler M, Masoodi M. Machine learning applications in precision medicine: overcoming challenges and unlocking potential. Trends Analyt Chem 2024; 179: 117872
• 53 Monaghan PJ, Robinson S, Rajdl D. et al. Practical guide for identifying unmet clinical needs for biomarkers. EJIFCC 2018; 29 (02) 129-137
• 54 Monaghan PJ, Lord SJ, St John A. et al; Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine. Biomarker development targeting unmet clinical needs. Clin Chim Acta 2016; 460: 211-219
• 55 Rutjes AWS, Reitsma JB, Di Nisio M, Smidt N, van Rijn JC, Bossuyt PMM. Evidence of bias and variation in diagnostic accuracy studies. CMAJ 2006; 174 (04) 469-476
• 56 van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol 2014; 14 (01) 137
• 57 Remeseiro B, Bolon-Canedo V. A review of feature selection methods in medical applications. Comput Biol Med 2019; 112: 103375
• 58 Connell J, Carlton J, Grundy A. et al. The importance of content and face validity in instrument development: lessons learnt from service users when developing the Recovering Quality of Life measure (ReQoL). Qual Life Res 2018; 27 (07) 1893-1902
• 59 AMA Augmented Intelligence Research. Accessed September 30, 2024 at: https://www.ama-assn.org/system/files/physician-ai-sentiment-report.pdf
• 60 Allgaier J, Mulansky L, Draelos RL, Pryss R. How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare. Artif Intell Med 2023; 143: 102616
• 61 Verma AA, Murray J, Greiner R. et al. Implementing machine learning in medicine. CMAJ 2021; 193 (34) E1351-E1357
• 62 Kasprzak P, Mitchell L, Kravchuk O, Timmins A. Six years of shiny in research - collaborative development of web tools in R. R J 2020; 12 (02) 155
• 63 ISSN 2349–7688 IJ of RRA. Efficient Way of Web Development Using Python and Flask. Accessed January 12, 2024 at: https://www.academia.edu/41538217/Efficient_Way_of_Web_Development_Using_Python_and_Flask
• 64 Cabitza F, Campagner A, Soares F. et al. The importance of being external. methodological insights for the external validation of machine learning models in medicine. Comput Methods Programs Biomed 2021; 208: 106288